Creative Evolutionary SystemsBy
- David Corne, University of Reading
- Peter Bentley, University College London, U.K.
The use of evolution for creative problem solving is one of the most exciting and potentially significant areas in computer science today. Evolutionary computation is a way of solving problems, or generating designs, using mechanisms derived from natural evolution. This book concentrates on applying important ideas in evolutionary computation to creative areas, such as art, music, architecture, and design. It shows how human interaction, new representations, and approaches such as open-ended evolution can extend the capabilities of evolutionary computation from optimization of existing solutions to innovation and the generation of entirely new and original solutions.
This book takes a fresh look at creativity, exploring what it is and how the actions of evolution can resemble it. Examples of novel evolved solutions are presented in a variety of creative disciplines. The editors have compiled contributions by leading researchers in each discipline.
If you are a savvy and curious computing professional, a computer-literate artist, musician or designer, or a specialist in evolutionary computation and its applications, you will find this a fascinating survey of the most interesting work being done in the area today.
Hardbound, 576 Pages
Published: July 2001
Imprint: Morgan Kaufmann
This volume shows the current state of the art, and the science, of evolutionary creativity. It shows what can--and equally important, what can't--be done at the turn of the new millennium. What will have been achieved by the turn of the next one is anyone's guess. Meanwhile, it's intriguing, it's instructive, it's difficult, and it's fun! --Margaret Boden, author of The Creative Mind, editor of Dimensions of Creativity
- About the Editors Foreword By Margaret BodenContributors Preface An Introduction to Creative Evolutionary SystemsBy Peter J. Bentley and David W. CorneIntroductionAI and CreativityEvolutionary Computation Creative Evolutionary Systems Is Evolution Creative?PART I - Evolutionary CreativityChapter 1 - Creativity in Evolution: Individuals, Interactions, and Environments By Tim Taylor1.1 Introduction1.2 Creativity and Opened-Ended Evolution 1.3 Design Issues 1.3.1 Von Neumann's Architecture for Self-Reproduction1.3.2 Tierra 1.3.3 Implicit versus Explicit Encoding 1.3.4 Ability to Perform Other Tasks 1.3.5 Embeddedness in the Arena of Competition and Richness of Interactions 1.3.6 Materiality1.4 A Full Specification For An Open-Ended Evolutionary Process 1.4.1 Waddington's Paradigm for an Evolutionary Process1.5 Conclusions Acknowledgments ReferencesChapter 2 - Recognizability of the Idea: The Evolutionary Process of ArgeniaBy Celestino Soddu2.1 Introduction2.2 Recognizability, Identity, And Complexity 2.3 Evolutionary Codes: Artificial DNA 2.4 Natural/Artificial Complexity 2.5 Giotto, A Medieval Idea In Evolution 2.6 Rome, Future Scenarios 2.7 Basilica, Generative Software To Design Complexity 2.8 Madrid and Milan, Generated Architecture 2.9 Argenìa, The Natural Industrial Object, And The Artificial Uniqueness Of Species 2.10 Argenìc Art: Picasso 2.11 Conclusions ReferencesChapter 3 - Breeding Aesthetic Objects: Art and Artificial Evolution By Mitchell Whitelaw3.1 Introduction 3.2 Breeding Aesthetic Objects 3.2.1 A Case StudySteven Rooke3.3 Breeding and Creation 3.3.1 Creative Agency and the Breeding Process3.3.2 The Evolved Aesthetic Object3.4 Limits3.5 Driessens and VerstappenAn Alternative Approach 3.6 Conclusions ReferencesChapter 4 - The Beer Can Theory of CreativityBy Liane Gabora4.1 Introduction 4.2 Culture As An Evolutionary Process 4.2.1 Variation and Convergence in Biology and Culture 4.2.2 Is More Than One Mind Necessary for Ideas to Evolve? 4.2.3 Meme and Variations: A Computer Model of Cultural Evolution 4.2.4 Breadth-First versus Depth-First Exploration 4.2.5 Dampening Arbitrary Associations and Forging Meaningful Ones4.3 Creativity as The Origin Of Culture 4.3.1 Theoretical Evidence 4.3.2 Archeological Evidence 4.3.3 Evidence from Animal Behavior 4.4 What Caused the Onset of Creativity? 4.5 Conclusions Acknowledgments References PART II Evolutionary Music Chapter 5 - GenJam: Evolution of a Jazz Improviser By John A. Biles5.1 Introduction 5.2 Overview and Architecture 5.3 Representations 5.4 Genetic Operators and Training 5.4.1 Crossover 5.4.2 Musically Meaningful Mutation 5.5 Real-Time Interaction 5.6 Conclusions References Chapter 6 - On the Origins and Evolution of Music in Virtual Worlds By Eduardo Reck Miranda6.1 Introduction 6.2 Evolutionary Modeling 6.2.1 Transformation and Selection 6.2.2 Coevolution 6.2.3 Self-organization 6.2.4 Level Formation 6.3 Evolving Sound With Cellular Automata 6.3.1 The Basics of Cellular Automata 6.3.2 The Cellular Automaton Used in Our System 6.3.3 The Synthesis Engine 6.4 Commentary On The Results 6.5 Conclusions Acknowledgments ReferencesChapter 7 - Vox Populi: Evolutionary Computation for Music Evolution By Artemis Moroni, Jônatas Manzolli, Fernando Von Zuben, and Ricardo Gudwin7.1 Introduction 7.2 Sound Attributes 7.3 Evolutionary Musical Cycle 7.3.1 The Voices Population 7.3.2 The Rhythm of the Evolution 7.4 Fitness Evaluation 7.4.1 The Consonance Criterion 7.4.2 Melodic Fitness 7.4.3 Harmonic Fitness 7.4.4 Voice Range Criterion 7.4.5 Musical Fitness 7.5 Interface And Parameter Control 7.6 Experiments 7.7 Conclusions Acknowledgments References Chapter 8 - The Sound GalleryAn Interactive A-Life Artwork By Sam Woolf and Adrian Thompson8.1 Introduction 8.2 Evolvable Hardware 8.2.1 Reconfigurable Chips 8.3 Gallery Setup 8.3.1 Setting 8.3.2 Sensing Systems 8.4 Contextualization: Artificial Life and Art 8.4.1 Evolutionary Algorithms and Visual Arts 8.4.2 Evolutionary Algorithms and Music 8.4.3 Interactive Genetic Art 8.4.4 Interactive, Adaptive, and Autonomous (Nongenetic) Artworks8.5 The Sound Gallery Algorithms 8.5.1 Two-Phase Hill-Climbing/ Island Model GA 8.5.2 Hill-climbing Phase 8.5.3 Island Model Genetic Algorithm Phase 8.5.4 The Need for Aging 8.5.5 Encoding Scheme 8.5.6 The Fitness Function 8.5.7 galSim 8.6 The Experiment 8.6.1 Results 8.7 Conclusions Acknowledgments References ContentsPART III Creative Evolutionary Design Chapter 9 - Creative Design and the Generative Evolutionary Paradigm By John Frazer9.1 Introduction 9.2 The Adaptive Model From Nature 9.3 The Generative Evolutionary Paradigm 9.4 Problems With The Paradigm 9.5 Concept Seeding Approach 9.6 The Reptile Demonstration 9.7 Universal State Space Modeler 9.8 Logic Fields 9.9 Returning to the Analogy with Nature 9.10 Conclusions References Chapter 10 - Genetic Programming: Biologically InspiredComputation That Exhibits Creativity in ProducingHuman-Competitive Results By John R. Koza, Forrest H. Bennett III, David Andre, andMartin A. Keane10.1 Introduction 10.2 Inventiveness And Creativity 10.3 Genetic Programming 10.4 Applying Genetic Programming To Circuit Synthesis 10.4.1 Campbell 1917 Ladder Filter Patent 10.4.2 Zobel 1925 "M-Derived Half Section" Patent 10.4.3 Cauer 1934-1936 Elliptic Filter Patents 10.4.4 Amplifier, Computational, Temperature-Sensing, Voltage Reference, andOther Circuits 10.5 Topology, Sizing, Placement, and Routing Of Circuits Contents10.6 Automatic Synthesis Of Controllers By Means Of Genetic Programming 10.6.1 Robust Controller for a Two-Lag Plant 10.7 The Illogical Nature Of Creativity And Evolution 10.8 Conclusions References Chapeter 11 - Toward a Symbiotic Coevolutionary Approach to Architecture By Helen Jackson11.1 Introduction 11.2 Lindenmayer Systems 11.2.1 Example L-Systems 11.2.2 The Isospatial Grid 11.2.3 Spatial Embryology 11.3 Artificial Selection 11.3.1 The Eyeball Test 11.4 Single-Goal Evolution 11.4.1 "Generic Function" as Fitness Function 11.4.2 Evolution toward Low i-Values 11.4.3 Structural Stability 11.4.4 Architecture As a Multigoal Task 11.4.5 Dual-Goal Evolution 11.5 Representation, Systems, And Symbiosis 11.5.1 Coevolution 11.5.2 Naïve Architectural Form Representation 11.5.3 Spatial Embryology 11.6 Conclusions Acknowledgments References Chapter 12 - Using Evolutionary Algorithms to Aid Designers of Architectural Structures By Peter von Buelow12.1 Introduction 12.2 Analysis Tools Vs. Design Tools 12.3 Advantages Of Evolutionary Systems In Design Contents12.3.1 Use of Populations 12.3.2 Recombination and Mutation 12.3.3 Wide Search of Design Space 12.3.4 No Knowledge of the Objective Function 12.3.5 Imitation of Human Design Process 12.3.6 Can Learn from Designer 12.4 Characteristics of an IGDT 12.4.1 Definition of the IGDT Concept 12.4.2 Relation of IGDT to Design Process12.5 Mechanics of an IGDT 12.6 IGDT Operation 12.6.1 Problem Definition 12.6.2 Initial IGDT Generation 12.6.3 Initial Generation with Designer Selection/Interaction 12.6.4 Second-Generation IGDT Response 12.6.5 Second-Generation Designer Interaction 12.6.6 Third Generation12.7 Conclusions Acknowledgments ReferencesPART IV Evolutionary ArtChapter 13 - Eons of Genetically Evolved Algorithmic Images By Steven Rooke13.1 Introduction 13.2 Using GP for Art 13.2.1 Genetic Variation 13.2.2 Genetic Library 13.2.3 Functions and Node Internals 13.2.4 A Typical Run 13.3 Horizon Lines And Fantasy Landscapes 13.4 Genetic Fractals 13.4.1 Second-Order Subtleties of Orbit Trajectories during Iteration in the Complex Plane 13.5 The Genetic Cross Dissolve 13.6 What Is It? 13.6.1 Constraints of Color and Form 13.6.2 A Joyride for the Visual Cortex? 13.6.3 Approaching the Organic 13.7 Conclusions References Chapter 14 - Art, Robots, and Evolution as a Tool for Creativity By Luigi Pagliarini and Henrik Hautop Lund14.1 Introduction 14.2 The Social Context Of Electronics 14.2.1 Where Electronics Acts 14.2.2 How Technology Influences Art (the World) 14.2.3 How Technology Gets Feedback (from Art and the World) 14.3 What Artist? 14.3.1 Two Different Concepts or Aspects of the Artist14.3.2 Art and Human Language: The "Immaterial" Artist 14.3.3 Art and Human Technique: The "Material" Artist 14.4 Electronic Art 14.4.1 A New Electronic Space 14.4.2 The "Material" Electronic Artist 14.4.3 The "Immaterial" Artist and the Uses of Electronics 14.4.4 ExampleThe Artificial Painter 14.5 Alive Art 14.5.1 Other Artistic Movements Based on Electronics 14.5.2 Alive Art 14.5.3 The Aliver 14.5.4 The "Alive Art Effect" 14.5.5 ExampleLEGO Robot Artists 14.6 Conclusions References Chapter 15 - Stepping Stones in the Mist By Paul Brown15.1 Introduction 15.2 On My Approach as an ArtistA Disclaimer 15.3 Major Influences 15.4 Historical Work1960s and 1970s 15.5 Early Computer Work 15.6 Recent Work 15.7 Current And Future Directions 15.8 Conclusions Acknowledgments References Chapter 16 - Evolutionary Generation of Faces 409By Peter J. B. Hancock and Charlie D. Frowd16.1 Introduction 16.1.1 Eigenfaces 16.1.2 Evolutionary Face Generator System 16.2 Testing 16.2.1 Apparatus 16.2.2 Generation of Face Images 16.2.3 Evolutionary Algorithm 16.2.4 Participants 16.3 Results 16.4 Discussion 16.5 Conclusions Acknowledgments References Chapter 17 - The Escher Evolver: Evolution to the People By A. E. Eiben, R. Nabuurs, and I. Booij17.1 Introduction 17.2 The Mathematical System Behind Escher's Tiling 17.3 Evolutionary Algorithm Design 17.3.1 Representation 17.3.2 Ground Shape and Transformation System 17.3.3 Genetic Operators: Mutation and Crossover 17.3.4 Selection Mechanism 17.4 Implementation and The Working of The System 17.4.1 Stand-Alone Version 17.4.2 First Networked Version 17.4.3 Second Networked Version 17.5 Conclusions Acknowledgments References PART V Evolutionary Innovation Chapter 18 - The Genetic Algorithm as a Discovery Engine: StrangeCircuits and New Principles By Julian F. Miller, Tatiana Kalganova, Natalia Lipnitskaya, and Dominic Job18.1 Introduction 18.2 The Space of All Representations 18.3 Evolutionary Algorithms That Assemble Electronic Circuits From ACollection of Available Components 18.3.1 Binary Circuit Symbols 18.3.2 Multiple-Valued Circuits 18.4 Results 18.4.1 One-Bit Adder 18.4.2 Two-Bit Adder 18.4.3 Two-Bit Multiplier 18.4.4 Three-Bit Multiplier 18.4.5 Multiple-Valued One-Digit Adder with Carry 18.5 Fingerprinting and Principle Extraction 18.6 Conclusions References Chapter 19 - Discovering Novel Fighter Combat Maneuvers:Simulating Test Pilot Creativity By R. E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, and R. K. Mehra19.1 Introduction 19.2 Fighter Aircraft Maneuvering 19.3 Genetics-Based Machine Learning 19.3.1 Learning Classifier Systems 19.3.2 The LCS Used Here 19.4 "One-Sided Learning" Results 19.5 "Two-Sided Learning" Results 19.6 Differences In Goals And Techniques 19.6.1 Implications of This Goal 19.7 Conclusions Acknowledgments References Chapter 20 - Innovative Antenna Design Using Genetic Algorithms By Derek S. Linden20.1 Introduction 20.2 Antenna Basics 20.3 Conventional Designs and Unconventional Applications: The Yagi-Uda Antenna 20.4 Unconventional Designs and Conventional Applications: Crooked-WireAnd Treelike Genetic Antennas 20.4.1 The Crooked-Wire Genetic Antenna 20.4.2 Treelike Genetic Antennas 20.5 Conclusions References Chapter 21 - Evolutionary Techniques in Physical Robotics By Jordan B. Pollack, Hod Lipson, Sevan Ficici, Pablo Funes,Greg Hornby, and Richard A. Watson21.1 Introduction 21.2 Coevolution 21.3 Research Thrusts 21.4 Evolution In Simulation 21.5 Buildable Simulation 21.6 Evolution and Construction of Electromechanical Systems 21.7 Embodied Evolution 21.8 Conclusions Acknowledgments ReferencesChapter 22 - Patenting of Novel Molecules Designed via Evolutionary Search By Shail Patel, Ian Stott, Manmohan Bhakoo, and Peter Elliott22.1 Introduction 22.2 Design Cycle 22.3 Hypothesis: Mechanism Of Action 22.4 Experimental Measures And Modeling Techniques 22.4.1 Molecular Modeling 22.4.2 Neural Networks 22.5 Evolution 22.6 Patent Application 22.6.1 Comparing Patent Spaces 22.7 Conclusions References Index